Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory101.3 B

Variable types

Numeric3
Text4
Categorical5

Alerts

CHNNEL_CL_NM has constant value ""Constant
UPPER_CTGRY_NM has constant value ""Constant
LWPRT_CTGRY_NM has constant value ""Constant
SEQ_NO is highly overall correlated with NTCE_DEHigh correlation
DPI_VALUE is highly overall correlated with LIKE_COHigh correlation
LIKE_CO is highly overall correlated with DPI_VALUEHigh correlation
NTCE_DE is highly overall correlated with SEQ_NOHigh correlation
COMMENT_CO is highly imbalanced (91.9%)Imbalance
SEQ_NO has unique valuesUnique
CNTNTS_ID has unique valuesUnique
CNTNTS_URL has unique valuesUnique
DPI_VALUE has 34 (34.0%) zerosZeros
LIKE_CO has 35 (35.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:07:46.344377
Analysis finished2023-12-10 10:07:51.345683
Duration5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1262768.9
Minimum1261673
Maximum1263852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:51.468704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1261673
5-th percentile1261795.9
Q11262186.2
median1262697
Q31263313.2
95-th percentile1263789.1
Maximum1263852
Range2179
Interquartile range (IQR)1127

Descriptive statistics

Standard deviation659.85187
Coefficient of variation (CV)0.00052254366
Kurtosis-1.275305
Mean1262768.9
Median Absolute Deviation (MAD)555
Skewness0.097834453
Sum1.2627689 × 108
Variance435404.49
MonotonicityNot monotonic
2023-12-10T19:07:51.725190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1261673 1
 
1.0%
1263055 1
 
1.0%
1263308 1
 
1.0%
1263281 1
 
1.0%
1263289 1
 
1.0%
1263276 1
 
1.0%
1263304 1
 
1.0%
1263209 1
 
1.0%
1263229 1
 
1.0%
1263228 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1261673 1
1.0%
1261684 1
1.0%
1261708 1
1.0%
1261752 1
1.0%
1261755 1
1.0%
1261798 1
1.0%
1261815 1
1.0%
1261861 1
1.0%
1261878 1
1.0%
1261896 1
1.0%
ValueCountFrequency (%)
1263852 1
1.0%
1263826 1
1.0%
1263805 1
1.0%
1263792 1
1.0%
1263791 1
1.0%
1263789 1
1.0%
1263787 1
1.0%
1263786 1
1.0%
1263766 1
1.0%
1263761 1
1.0%

CNTNTS_ID
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:52.083858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3200
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row85020016fc1111ebb533002b67f7b0e1
2nd row9496dc41fc1111eb9166002b67f7b0e1
3rd row8879616afc1111ebbd05002b67f7b0e1
4th row940b0ecefc1111ebabd0002b67f7b0e1
5th row8ce331a6fc1111eba5ea002b67f7b0e1
ValueCountFrequency (%)
85020016fc1111ebb533002b67f7b0e1 1
 
1.0%
863a5bfafc1211ebb46c002b67f7b0e1 1
 
1.0%
b3fd68c2fc1211ebb498002b67f7b0e1 1
 
1.0%
b566cfa8fc1211ebad8c002b67f7b0e1 1
 
1.0%
b32501ccfc1211ebb7be002b67f7b0e1 1
 
1.0%
b7dde1bafc1211eba4b7002b67f7b0e1 1
 
1.0%
a6ca2cf4fc1211eb80a7002b67f7b0e1 1
 
1.0%
aa0f6c7afc1211eba822002b67f7b0e1 1
 
1.0%
a9e65d20fc1211ebb9c7002b67f7b0e1 1
 
1.0%
9ad5fa20fc1211eb9098002b67f7b0e1 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:07:52.939027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 505
15.8%
b 393
12.3%
0 361
11.3%
e 270
8.4%
f 264
8.2%
7 259
8.1%
2 236
7.4%
6 178
 
5.6%
c 178
 
5.6%
a 97
 
3.0%
Other values (6) 459
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1912
59.8%
Lowercase Letter 1288
40.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 505
26.4%
0 361
18.9%
7 259
13.5%
2 236
12.3%
6 178
 
9.3%
9 89
 
4.7%
8 84
 
4.4%
5 72
 
3.8%
4 65
 
3.4%
3 63
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
b 393
30.5%
e 270
21.0%
f 264
20.5%
c 178
13.8%
a 97
 
7.5%
d 86
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1912
59.8%
Latin 1288
40.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 505
26.4%
0 361
18.9%
7 259
13.5%
2 236
12.3%
6 178
 
9.3%
9 89
 
4.7%
8 84
 
4.4%
5 72
 
3.8%
4 65
 
3.4%
3 63
 
3.3%
Latin
ValueCountFrequency (%)
b 393
30.5%
e 270
21.0%
f 264
20.5%
c 178
13.8%
a 97
 
7.5%
d 86
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 505
15.8%
b 393
12.3%
0 361
11.3%
e 270
8.4%
f 264
8.2%
7 259
8.1%
2 236
7.4%
6 178
 
5.6%
c 178
 
5.6%
a 97
 
3.0%
Other values (6) 459
14.3%

CHNNEL_CL_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
blog
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblog
2nd rowblog
3rd rowblog
4th rowblog
5th rowblog

Common Values

ValueCountFrequency (%)
blog 100
100.0%

Length

2023-12-10T19:07:53.184120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:53.367298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
blog 100
100.0%

CHNNEL_NM
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:53.940060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length26
Mean length11.44
Min length4

Characters and Unicode

Total characters1144
Distinct characters339
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)88.0%

Sample

1st row모모와이_MOMOY
2nd row추천하는남자
3rd row호론 HO LOAN
4th row덕질 깔롱맘
5th row냥 일기는 너무 식상해
ValueCountFrequency (%)
추천하는남자 3
 
1.3%
song 3
 
1.3%
이야기 3
 
1.3%
함께 3
 
1.3%
3
 
1.3%
특명 3
 
1.3%
식민사학을 3
 
1.3%
조선총독부 3
 
1.3%
폭파하라 3
 
1.3%
chesca 2
 
0.8%
Other values (199) 209
87.8%
2023-12-10T19:07:54.847926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
 
12.1%
의 19
 
1.7%
기 17
 
1.5%
- 16
 
1.4%
n 15
 
1.3%
이 13
 
1.1%
e 13
 
1.1%
원 13
 
1.1%
사 12
 
1.0%
는 12
 
1.0%
Other values (329) 876
76.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 752
65.7%
Space Separator 138
 
12.1%
Lowercase Letter 119
 
10.4%
Uppercase Letter 51
 
4.5%
Decimal Number 25
 
2.2%
Other Punctuation 17
 
1.5%
Dash Punctuation 16
 
1.4%
Other Symbol 13
 
1.1%
Open Punctuation 4
 
0.3%
Math Symbol 3
 
0.3%
Other values (3) 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 19
 
2.5%
기 17
 
2.3%
이 13
 
1.7%
원 13
 
1.7%
사 12
 
1.6%
는 12
 
1.6%
오 12
 
1.6%
하 11
 
1.5%
아 11
 
1.5%
을 9
 
1.2%
Other values (259) 623
82.8%
Lowercase Letter
ValueCountFrequency (%)
n 15
12.6%
e 13
10.9%
o 11
9.2%
a 10
 
8.4%
i 10
 
8.4%
s 9
 
7.6%
g 7
 
5.9%
d 7
 
5.9%
t 6
 
5.0%
m 5
 
4.2%
Other values (11) 26
21.8%
Uppercase Letter
ValueCountFrequency (%)
O 8
15.7%
E 6
11.8%
R 4
 
7.8%
H 4
 
7.8%
A 4
 
7.8%
T 3
 
5.9%
S 3
 
5.9%
L 3
 
5.9%
U 2
 
3.9%
N 2
 
3.9%
Other values (8) 12
23.5%
Other Punctuation
ValueCountFrequency (%)
. 4
23.5%
, 2
11.8%
! 2
11.8%
' 2
11.8%
? 2
11.8%
& 1
 
5.9%
、 1
 
5.9%
。 1
 
5.9%
/ 1
 
5.9%
* 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
7 6
24.0%
2 5
20.0%
3 4
16.0%
6 3
12.0%
0 2
 
8.0%
8 2
 
8.0%
5 1
 
4.0%
9 1
 
4.0%
1 1
 
4.0%
Other Symbol
ValueCountFrequency (%)
♡ 10
76.9%
❤ 2
 
15.4%
★ 1
 
7.7%
Open Punctuation
ValueCountFrequency (%)
[ 2
50.0%
( 2
50.0%
Close Punctuation
ValueCountFrequency (%)
] 2
66.7%
) 1
33.3%
Space Separator
ValueCountFrequency (%)
138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 752
65.7%
Common 222
 
19.4%
Latin 170
 
14.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 19
 
2.5%
기 17
 
2.3%
이 13
 
1.7%
원 13
 
1.7%
사 12
 
1.6%
는 12
 
1.6%
오 12
 
1.6%
하 11
 
1.5%
아 11
 
1.5%
을 9
 
1.2%
Other values (259) 623
82.8%
Latin
ValueCountFrequency (%)
n 15
 
8.8%
e 13
 
7.6%
o 11
 
6.5%
a 10
 
5.9%
i 10
 
5.9%
s 9
 
5.3%
O 8
 
4.7%
g 7
 
4.1%
d 7
 
4.1%
t 6
 
3.5%
Other values (29) 74
43.5%
Common
ValueCountFrequency (%)
138
62.2%
- 16
 
7.2%
♡ 10
 
4.5%
7 6
 
2.7%
2 5
 
2.3%
3 4
 
1.8%
. 4
 
1.8%
6 3
 
1.4%
~ 3
 
1.4%
❤ 2
 
0.9%
Other values (21) 31
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 752
65.7%
ASCII 377
33.0%
Misc Symbols 11
 
1.0%
Dingbats 2
 
0.2%
None 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
36.6%
- 16
 
4.2%
n 15
 
4.0%
e 13
 
3.4%
o 11
 
2.9%
a 10
 
2.7%
i 10
 
2.7%
s 9
 
2.4%
O 8
 
2.1%
g 7
 
1.9%
Other values (55) 140
37.1%
Hangul
ValueCountFrequency (%)
의 19
 
2.5%
기 17
 
2.3%
이 13
 
1.7%
원 13
 
1.7%
사 12
 
1.6%
는 12
 
1.6%
오 12
 
1.6%
하 11
 
1.5%
아 11
 
1.5%
을 9
 
1.2%
Other values (259) 623
82.8%
Misc Symbols
ValueCountFrequency (%)
♡ 10
90.9%
★ 1
 
9.1%
Dingbats
ValueCountFrequency (%)
❤ 2
100.0%
None
ValueCountFrequency (%)
、 1
50.0%
。 1
50.0%

TITLE_NM
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:55.302454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length98
Median length50.5
Mean length32.1
Min length6

Characters and Unicode

Total characters3210
Distinct characters499
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row세븐틴은 KBS 가요대축제 대축제의 대기조였다 방역 기준 무시
2nd row법카로 최애 콘서트 열어준 덕후 김태호pd
3rd row고창 청보리밭 축제 45월축제 고창여행 전북여행 국내여행 데이트하기좋은곳 산책 힐링
4th row지민 레이블콘서트
5th row2020 열정 연말결산 ep32020 서울지식이음축제포럼 자원활동가 도돌이
ValueCountFrequency (%)
콘서트 25
 
3.3%
공연 14
 
1.8%
축제 11
 
1.4%
영탁 7
 
0.9%
서울콘서트 6
 
0.8%
임영웅 6
 
0.8%
비하인드 5
 
0.7%
2020 5
 
0.7%
샌드아트 4
 
0.5%
미니 4
 
0.5%
Other values (602) 677
88.6%
2023-12-10T19:07:56.225563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
664
 
20.7%
트 65
 
2.0%
연 60
 
1.9%
서 60
 
1.9%
공 56
 
1.7%
2 52
 
1.6%
0 43
 
1.3%
콘 43
 
1.3%
제 39
 
1.2%
1 36
 
1.1%
Other values (489) 2092
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2219
69.1%
Space Separator 664
 
20.7%
Decimal Number 173
 
5.4%
Uppercase Letter 90
 
2.8%
Lowercase Letter 64
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 65
 
2.9%
연 60
 
2.7%
서 60
 
2.7%
공 56
 
2.5%
콘 43
 
1.9%
제 39
 
1.8%
대 34
 
1.5%
이 32
 
1.4%
축 29
 
1.3%
기 28
 
1.3%
Other values (435) 1773
79.9%
Uppercase Letter
ValueCountFrequency (%)
P 8
 
8.9%
T 8
 
8.9%
K 7
 
7.8%
S 6
 
6.7%
O 5
 
5.6%
L 5
 
5.6%
V 5
 
5.6%
E 5
 
5.6%
X 5
 
5.6%
N 5
 
5.6%
Other values (12) 31
34.4%
Lowercase Letter
ValueCountFrequency (%)
e 10
15.6%
i 9
14.1%
c 5
 
7.8%
o 3
 
4.7%
v 3
 
4.7%
u 3
 
4.7%
l 3
 
4.7%
t 3
 
4.7%
d 3
 
4.7%
n 3
 
4.7%
Other values (11) 19
29.7%
Decimal Number
ValueCountFrequency (%)
2 52
30.1%
0 43
24.9%
1 36
20.8%
3 10
 
5.8%
5 8
 
4.6%
6 8
 
4.6%
4 7
 
4.0%
7 4
 
2.3%
9 3
 
1.7%
8 2
 
1.2%
Space Separator
ValueCountFrequency (%)
664
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2219
69.1%
Common 837
 
26.1%
Latin 154
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 65
 
2.9%
연 60
 
2.7%
서 60
 
2.7%
공 56
 
2.5%
콘 43
 
1.9%
제 39
 
1.8%
대 34
 
1.5%
이 32
 
1.4%
축 29
 
1.3%
기 28
 
1.3%
Other values (435) 1773
79.9%
Latin
ValueCountFrequency (%)
e 10
 
6.5%
i 9
 
5.8%
P 8
 
5.2%
T 8
 
5.2%
K 7
 
4.5%
S 6
 
3.9%
O 5
 
3.2%
c 5
 
3.2%
L 5
 
3.2%
V 5
 
3.2%
Other values (33) 86
55.8%
Common
ValueCountFrequency (%)
664
79.3%
2 52
 
6.2%
0 43
 
5.1%
1 36
 
4.3%
3 10
 
1.2%
5 8
 
1.0%
6 8
 
1.0%
4 7
 
0.8%
7 4
 
0.5%
9 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2202
68.6%
ASCII 991
30.9%
Compat Jamo 17
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
664
67.0%
2 52
 
5.2%
0 43
 
4.3%
1 36
 
3.6%
e 10
 
1.0%
3 10
 
1.0%
i 9
 
0.9%
P 8
 
0.8%
5 8
 
0.8%
6 8
 
0.8%
Other values (44) 143
 
14.4%
Hangul
ValueCountFrequency (%)
트 65
 
3.0%
연 60
 
2.7%
서 60
 
2.7%
공 56
 
2.5%
콘 43
 
2.0%
제 39
 
1.8%
대 34
 
1.5%
이 32
 
1.5%
축 29
 
1.3%
기 28
 
1.3%
Other values (430) 1756
79.7%
Compat Jamo
ValueCountFrequency (%)
ㅇ 10
58.8%
ㅠ 2
 
11.8%
ㅅ 2
 
11.8%
ㅌ 2
 
11.8%
ㄹ 1
 
5.9%

NTCE_DE
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2021-01-12
 
7
2021-01-30
 
7
2021-01-08
 
6
2021-01-14
 
5
2021-01-23
 
5
Other values (26)
70 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row2021-01-01
2nd row2021-01-02
3rd row2021-01-02
4th row2021-01-02
5th row2021-01-02

Common Values

ValueCountFrequency (%)
2021-01-12 7
 
7.0%
2021-01-30 7
 
7.0%
2021-01-08 6
 
6.0%
2021-01-14 5
 
5.0%
2021-01-23 5
 
5.0%
2021-01-04 5
 
5.0%
2021-01-07 5
 
5.0%
2021-01-20 4
 
4.0%
2021-01-02 4
 
4.0%
2021-01-27 4
 
4.0%
Other values (21) 48
48.0%

Length

2023-12-10T19:07:56.470658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-12 7
 
7.0%
2021-01-30 7
 
7.0%
2021-01-08 6
 
6.0%
2021-01-14 5
 
5.0%
2021-01-23 5
 
5.0%
2021-01-04 5
 
5.0%
2021-01-07 5
 
5.0%
2021-01-20 4
 
4.0%
2021-01-02 4
 
4.0%
2021-01-27 4
 
4.0%
Other values (21) 48
48.0%

DPI_VALUE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.906
Minimum0
Maximum29
Zeros34
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:56.718112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4
Q31.85
95-th percentile7.63
Maximum29
Range29
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation4.0302347
Coefficient of variation (CV)2.1144988
Kurtosis23.68935
Mean1.906
Median Absolute Deviation (MAD)0.4
Skewness4.3641121
Sum190.6
Variance16.242792
MonotonicityNot monotonic
2023-12-10T19:07:56.948309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 34
34.0%
0.4 11
 
11.0%
0.2 8
 
8.0%
0.8 6
 
6.0%
0.6 4
 
4.0%
2.0 3
 
3.0%
2.8 3
 
3.0%
1.2 3
 
3.0%
1.6 3
 
3.0%
1.0 2
 
2.0%
Other values (16) 23
23.0%
ValueCountFrequency (%)
0.0 34
34.0%
0.2 8
 
8.0%
0.4 11
 
11.0%
0.6 4
 
4.0%
0.8 6
 
6.0%
1.0 2
 
2.0%
1.2 3
 
3.0%
1.4 2
 
2.0%
1.6 3
 
3.0%
1.8 2
 
2.0%
ValueCountFrequency (%)
29.0 1
1.0%
19.4 1
1.0%
13.6 1
1.0%
8.4 1
1.0%
8.2 1
1.0%
7.6 2
2.0%
5.8 2
2.0%
5.6 2
2.0%
5.2 1
1.0%
4.8 1
1.0%

LIKE_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.49
Minimum0
Maximum145
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:57.210573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39.25
95-th percentile38.15
Maximum145
Range145
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation20.166226
Coefficient of variation (CV)2.1249975
Kurtosis23.645941
Mean9.49
Median Absolute Deviation (MAD)2
Skewness4.3595438
Sum949
Variance406.67667
MonotonicityNot monotonic
2023-12-10T19:07:57.555404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 35
35.0%
2 11
 
11.0%
1 8
 
8.0%
4 5
 
5.0%
3 4
 
4.0%
10 3
 
3.0%
14 3
 
3.0%
6 3
 
3.0%
8 3
 
3.0%
5 2
 
2.0%
Other values (16) 23
23.0%
ValueCountFrequency (%)
0 35
35.0%
1 8
 
8.0%
2 11
 
11.0%
3 4
 
4.0%
4 5
 
5.0%
5 2
 
2.0%
6 3
 
3.0%
7 2
 
2.0%
8 3
 
3.0%
9 2
 
2.0%
ValueCountFrequency (%)
145 1
1.0%
97 1
1.0%
68 1
1.0%
42 1
1.0%
41 1
1.0%
38 2
2.0%
29 2
2.0%
28 2
2.0%
26 1
1.0%
24 1
1.0%

COMMENT_CO
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
99 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99
99.0%
1 1
 
1.0%

Length

2023-12-10T19:07:57.828073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:57.996449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 99
99.0%
1 1
 
1.0%

CNTNTS_URL
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:58.400694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length75
Mean length70.82
Min length67

Characters and Unicode

Total characters7082
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowhttps://blog.naver.com/PostView.nhn?blogId=gomsune1&logNo=222192449984
2nd rowhttps://blog.naver.com/PostView.nhn?blogId=begin22again&logNo=222193568855
3rd rowhttps://blog.naver.com/PostView.nhn?blogId=passion8259&logNo=222193859076
4th rowhttps://blog.naver.com/PostView.nhn?blogId=ozoo0829&logNo=222193113261
5th rowhttps://blog.naver.com/PostView.nhn?blogId=2jjiimm2&logNo=222193931752
ValueCountFrequency (%)
https://blog.naver.com/postview.nhn?blogid=gomsune1&logno=222192449984 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=panic1009&logno=222213752781 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=juys3000&logno=222217100963 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=khjtwin&logno=222217004156 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=dnotecom&logno=222217646491 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=moribe&logno=222217180583 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=yskim138&logno=222215601373 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=perfectgohan&logno=222216153767 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=theartlounge&logno=222216366059 1
 
1.0%
https://blog.naver.com/postview.nhn?blogid=tr__h_c&logno=222214525888 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:07:59.192093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 648
 
9.1%
2 495
 
7.0%
n 355
 
5.0%
l 327
 
4.6%
t 326
 
4.6%
g 325
 
4.6%
/ 300
 
4.2%
. 300
 
4.2%
e 248
 
3.5%
s 242
 
3.4%
Other values (37) 3516
49.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4138
58.4%
Decimal Number 1438
 
20.3%
Other Punctuation 900
 
12.7%
Uppercase Letter 400
 
5.6%
Math Symbol 200
 
2.8%
Connector Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 648
15.7%
n 355
 
8.6%
l 327
 
7.9%
t 326
 
7.9%
g 325
 
7.9%
e 248
 
6.0%
s 242
 
5.8%
h 227
 
5.5%
b 206
 
5.0%
a 149
 
3.6%
Other values (16) 1085
26.2%
Decimal Number
ValueCountFrequency (%)
2 495
34.4%
1 167
 
11.6%
0 157
 
10.9%
7 96
 
6.7%
6 92
 
6.4%
9 91
 
6.3%
5 91
 
6.3%
3 91
 
6.3%
8 83
 
5.8%
4 75
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/ 300
33.3%
. 300
33.3%
& 100
 
11.1%
: 100
 
11.1%
? 100
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
N 100
25.0%
I 100
25.0%
V 100
25.0%
P 100
25.0%
Math Symbol
ValueCountFrequency (%)
= 200
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4538
64.1%
Common 2544
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 648
14.3%
n 355
 
7.8%
l 327
 
7.2%
t 326
 
7.2%
g 325
 
7.2%
e 248
 
5.5%
s 242
 
5.3%
h 227
 
5.0%
b 206
 
4.5%
a 149
 
3.3%
Other values (20) 1485
32.7%
Common
ValueCountFrequency (%)
2 495
19.5%
/ 300
11.8%
. 300
11.8%
= 200
 
7.9%
1 167
 
6.6%
0 157
 
6.2%
& 100
 
3.9%
: 100
 
3.9%
? 100
 
3.9%
7 96
 
3.8%
Other values (7) 529
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 648
 
9.1%
2 495
 
7.0%
n 355
 
5.0%
l 327
 
4.6%
t 326
 
4.6%
g 325
 
4.6%
/ 300
 
4.2%
. 300
 
4.2%
e 248
 
3.5%
s 242
 
3.4%
Other values (37) 3516
49.6%

UPPER_CTGRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
문화
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row문화
2nd row문화
3rd row문화
4th row문화
5th row문화

Common Values

ValueCountFrequency (%)
문화 100
100.0%

Length

2023-12-10T19:07:59.445206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:59.721064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화 100
100.0%

LWPRT_CTGRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
미술
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미술
2nd row미술
3rd row미술
4th row미술
5th row미술

Common Values

ValueCountFrequency (%)
미술 100
100.0%

Length

2023-12-10T19:07:59.979070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:00.133402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미술 100
100.0%

Interactions

2023-12-10T19:07:50.065209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:48.953659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.542136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:50.624271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.152454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.721148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:50.772995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.354000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.903354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:00.243408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOCNTNTS_IDCHNNEL_NMTITLE_NMNTCE_DEDPI_VALUELIKE_COCOMMENT_COCNTNTS_URL
SEQ_NO1.0001.0000.9231.0000.9980.0000.0000.0001.000
CNTNTS_ID1.0001.0001.0001.0001.0001.0001.0001.0001.000
CHNNEL_NM0.9231.0001.0000.9970.9771.0001.0001.0001.000
TITLE_NM1.0001.0000.9971.0001.0001.0001.0001.0001.000
NTCE_DE0.9981.0000.9771.0001.0000.5710.5710.2291.000
DPI_VALUE0.0001.0001.0001.0000.5711.0001.0000.0001.000
LIKE_CO0.0001.0001.0001.0000.5711.0001.0000.0001.000
COMMENT_CO0.0001.0001.0001.0000.2290.0000.0001.0001.000
CNTNTS_URL1.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:08:00.452298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NTCE_DECOMMENT_CO
NTCE_DE1.0000.154
COMMENT_CO0.1541.000
2023-12-10T19:08:00.604335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NODPI_VALUELIKE_CONTCE_DECOMMENT_CO
SEQ_NO1.0000.1790.1890.8060.000
DPI_VALUE0.1791.0000.9880.2370.000
LIKE_CO0.1890.9881.0000.2370.000
NTCE_DE0.8060.2370.2371.0000.154
COMMENT_CO0.0000.0000.0000.1541.000

Missing values

2023-12-10T19:07:50.986183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:07:51.244462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SEQ_NOCNTNTS_IDCHNNEL_CL_NMCHNNEL_NMTITLE_NMNTCE_DEDPI_VALUELIKE_COCOMMENT_COCNTNTS_URLUPPER_CTGRY_NMLWPRT_CTGRY_NM
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SEQ_NOCNTNTS_IDCHNNEL_CL_NMCHNNEL_NMTITLE_NMNTCE_DEDPI_VALUELIKE_COCOMMENT_COCNTNTS_URLUPPER_CTGRY_NMLWPRT_CTGRY_NM
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